docstr: Converts original predictions of the detection model to a list of
  prediction.ObjectPrediction object. Should be called after perform_inference().
  Args:
      shift_amount: list
          To shift the box and mask predictions from sliced image to full sized image, should be in the form of [shift_x, shift_y]
      full_shape: list
          Size of the full image after shifting, should be in the form of [height, width]
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code: def convert_original_predictions(
      self,
      shift_amount: Optional[List[int]] = [0, 0],
      full_shape: Optional[List[int]] = None,
  ):
      """
      Converts original predictions of the detection model to a list of
      prediction.ObjectPrediction object. Should be called after perform_inference().
      Args:
          shift_amount: list
              To shift the box and mask predictions from sliced image to full sized image, should be in the form of [shift_x, shift_y]
          full_shape: list
              Size of the full image after shifting, should be in the form of [height, width]
      """
      self._create_object_prediction_list_from_original_predictions(
          shift_amount_list=shift_amount,
          full_shape_list=full_shape,
      )
      if self.category_remapping:
          self._apply_category_remapping()
  
file_path: sahi/sahi/models/base.py
imports: from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  from sahi.utils.import_utils import is_available
  from sahi.utils.torch import select_device
label_desc: Use image node_id 10 for calling the DetectionModel obj's underlying member method code with example usage: obj.convert_original_predictions(shift_amount, full_shape) without return types